autointent.modules.scoring.BERTLoRAScorer#

class autointent.modules.scoring.BERTLoRAScorer(classification_model_config=None, num_train_epochs=3, batch_size=8, learning_rate=5e-05, seed=0, report_to='none', print_progress=False, **lora_kwargs)#

Bases: autointent.modules.scoring._bert.BertScorer

BERTLoRAScorer class for transformer-based classification with LoRA (Low-Rank Adaptation).

Parameters:

  • classification_model_config (autointent.configs.HFModelConfig | str | dict[str, Any] | None) – Config of the base transformer model (HFModelConfig, str, or dict)

  • num_train_epochs (int) – Number of training epochs (default: 3)

  • batch_size (int) – Batch size for training (default: 8)

  • learning_rate (float) – Learning rate for training (default: 5e-5)

  • seed (int) – Random seed for reproducibility (default: 0)

  • report_to (autointent._callbacks.REPORTERS_NAMES | Literal['none']) – Reporting tool for training logs

  • **lora_kwargs (Any) – Arguments for LoraConfig

  • print_progress (bool)

  • **lora_kwargs

Example:#

from autointent.modules import BERTLoRAScorer

# Initialize scorer with LoRA configuration
scorer = BERTLoRAScorer(
    classification_model_config="bert-base-uncased",
    num_train_epochs=3,
    batch_size=8,
    learning_rate=5e-5,
    seed=42,
    r=8,  # LoRA rank
    lora_alpha=16,  # LoRA alpha
)

# Training data
utterances = ["This is great!", "I didn't like it", "Awesome product", "Poor quality"]
labels = [1, 0, 1, 0]  # Binary classification

# Fit the model
scorer.fit(utterances, labels)

# Make predictions
test_utterances = ["Good product", "Not worth it"]
probabilities = scorer.predict(test_utterances)
name = 'lora'#

Name of the module to reference in search space configuration.

classmethod from_context(context, classification_model_config=None, num_train_epochs=3, batch_size=8, learning_rate=5e-05, seed=0, **lora_kwargs)#

Initialize self from context.

Parameters:
Returns:

Initialized module

Return type:

BERTLoRAScorer

dump(path)#

Dump all data needed for inference.

Parameters:

path (str) – Path to dump

Return type:

None